Laser SLAM (Simultaneous Localization And Mapping) algorithm based on self-adaption unscented Kalman filter

Abstract

The invention discloses a laser SLAM (Simultaneous Localization And Mapping) algorithm based on self-adaption unscented Kalman filter. The algorithm is as follows: firstly utilizing a matching methodin a Hector SLAM algorithm to achieve locating on the basis of the matching optimization of a laser beam end and a known map, and reading laser data and performing filter transformation, thereby acquiring an estimated laser position and posture; utilizing the unscented Kalman filter algorithm to calculate the position and posture data of a nonlinear system on the basis of the estimated laser position and posture data and a speedometer, thereby acquiring a practical estimated position and posture; last, calculating a self-adaption vector, updating a measurement noise covariance matrix and usingthe self-adaption unscented Kalman filter algorithm for filtering the nonlinear system. According to the invention, higher locating and composition accuracy can be acquired under the condition of only depending on laser data, the local extreme value is avoided, the interference data scanned by laser is reduced or eliminated, the problem of unknown or changing characteristics of practical system noise or measurement noise is solved, the estimation effect of the nonlinear system is improved and the locating precision is promoted.

Description

technical field [0001] The invention belongs to the technical field of SLAM navigation and positioning algorithms, in particular to a laser SLAM algorithm based on an adaptive unscented Kalman filter. Background technique [0002] Instant Localization and Mapping (SLAM) technology was formally proposed at the IEEE Robotics and Automation Conference in 1986. After tortuous and long exploration, the development of SLAM technology has gradually matured. In recent years, with the introduction of optimization theory, SLAM technology has entered a new period of rapid development. In 2011, Stefan Kohlbrecher, Oskar von Stryk and others proposed a Hector SLAM algorithm based on optimization theory. Hector SLAM is an efficient and fast online SLAM algorithm. It can also obtain higher positioning when only relying on laser data. and compositional precision. However, the Hector SLAM algorithm still cannot obtain accurate positioning in environments with similar characteristics such a...

AI Technical Summary

Problems solved by technology

[0004] The optimal estimation of the unscented Kalman filter is based on the known characteristics of system noise and measurement noise, which is often difficult to meet in reality. The statistical characteristics of system noise and measurement noise are usually unknown, and may even change of

To sum up, the problems existing in the existing technology are: the statistical characteristics of the actual system noise and measurement noise are usually unknown, and may even change, which often leads to the result of falling into a local extremum, and the interference data detected by laser scanning is more, The final filtering result is not ideal, and the positioning accuracy is poor

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